A Joint Deterministic - Probabilistic Approach To Bulk System Reliability Assessment
Bibliographic record
Abstract
Most electric power utilities utilize probability methods in the planning and operation of generating capacity. Deterministic criteria, however, are usually used in the planning and operation of composite generation and transmission or bulk electric power systems. The most commonly applied deterministic criteria dictate that specific credible outages should not result in system failure. The traditional deterministic criterion used in bulk electric systems is known as the N-l security criterion, under which, the loss of any single BES component will not result in system failure. Deterministic techniques do not include an assessment of the actual system reliability and are inconsistent as they do not incorporate the probabilistic or stochastic nature of system behaviour and component failures. This paper introduces and illustrates the application of a joint deterministic-probabilistic (D-P) criterion for bulk electric system planning that can be applied using any software package developed and accepted for probabilistic assessment. The D-P concept is a deterministic framework that incorporates a probabilistic criterion. The paper illustrates the application of the conventional deterministic N-l, the basic probabilistic (P) and the D-P criteria to a published test system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".